Law
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
As language models (LMs) are widely utilized in personalized communication scenarios ( e.g., sending emails, writing social media posts) and endowed with a
MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding Xinyu Fang
The advent of large vision-language models (L VLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.
DAGER: Exact Gradient Inversion for Large Language Models
However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.
MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless responses when faced with malicious instructions.
End-to-End Ontology Learning with Large Language Models
Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.
WhodunitBench: Evaluating Large Multimodal Agents via Murder Mystery Games
Recently, large language models (LLMs) have achieved superior performance, empowering the development of large multimodal agents (LMAs).
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
Most of the literature in fair machine learning focuses on defining and achieving fairness criteria in the context of prediction, while not explicitly focusing on how these predictions may be used later on in the pipeline.